A computer simulation is a computer program which attempts to reduce the physical characteristics of an object, part, article, component, specimen, device or system (generally termed hereinafter “item”) to a virtual model comprising algorithmic and/or mathematical logic. A second set of algorithmic or mathematical logic representing physical stimuli are applied to the virtual model. The virtual model's response to the stimuli represents a prediction of how a physical specimen of the item would react to similar stimuli. Various commercial products are available to perform computer simulations including, among others, NASTRAN, which was developed at NASA and is widely used by industry; ANSYS, provided by ANSYS, Inc.; ABAQUS, provided by ABAQUS, Inc; and ALGOR, provided by ALGOR, Inc.
Computer simulations are particularly useful as design tools because they allow the characteristics of an item to be studied without generating a physical specimen of the item. Since no physical specimen is required, the characteristics of the item can be rapidly evaluated and altered, thereby providing designers with the ability to efficiently optimize the item. For example, a finite element analysis (FEA) simulation can provide a prediction of the stresses and stress distribution of a support beam under a certain load. This computed information can then be used to determine whether the design of the beam is adequate for its intended use.
Another common type of computer simulation is vibration simulation wherein a virtual model of an item is analyzed to predict how the item will function when subjected to various types of vibration, such as random vibration and sinusoidal vibration, over a range of frequencies. Vibration analysis can also be employed to determine resonant frequencies of the item. If the resonant frequencies are within the range of vibrations occurring in the expected operating environment of the item, then its design can be altered to forestall potential operational and reliability issues. For example, a position light mounted to a wing of an aircraft can be modeled and a simulation performed to study the stresses imposed upon it by vibrations in the wing. The resulting analysis can provide insight into, for instance, whether those vibrations will lead to premature failure of incandescent lamps used in the position light, or cause resonances that could lead to structural failure of the light's mounting points.
An important and often-overlooked aspect of computer simulation is consideration of how accurately the simulation predicts the characteristics of a physical specimen of the item. Without a comparative review of the prediction against actual test data for a physical specimen, a latent defect in an item's design may not manifest itself until the item has already been placed into service, with the result that the item may not have the desired performance or reliability. It is therefore desirable to validate the computer virtual model by obtaining a measure of the correlation of two frequency response functions (FRF), i.e., a comparison of actual test data to a computer simulation, in order to determine how accurately the simulation predicts the actual test result. A frequency response assurance criterion (FRAC) is typically used in the art to measure this correlation. However, FRAC focuses on the frequency of peaks in the curves and does not take into account the shape and amplitude of FRF curves. For example, FRAC correlation typically expands one or both of the frequency response functions being compared in order to line up their frequency peaks, thus distorting the shape information. In addition, FRAC does not take into account amplitude differences between the functions. As a result of these shortcomings, the FRAC correlation data may not be sufficiently accurate to determine how accurately the simulation predicts real-world performance. There is a need for a way to measure the correlation between two frequency response functions with greater accuracy than is presently available in the art.